The content available to networked computer users has increased significantly in recent years. Providers of such content include blogs, news sources, sports sources, weather sources, libraries, friends, universities, businesses, and the like. Many of these content providers provide new or changed content almost regularly.
Because of the large amount of changing content, users often seek mechanisms that help them manage access and use of the content sources that interest them. One such mechanism uses a Really Simple Syndication (RSS) feed. Generally, RSS provides web content or summaries of web content together with links to the full versions of the content, and other meta-data. This information is delivered as an Extensible Markup Language (XML) file typically called an RSS feed, web feed, RSS stream, or RSS channel. RSS feeds enable a user to subscribe to a content provider's website, or the like, and receive a content feed in a defined format. Other services can provide an alert indicating when a change to the content has occurred. However, as the number of RSS feeds available over a network increases, a subscriber may become increasingly overwhelmed. Also, managing large numbers of RSS feeds for potentially millions of subscribers has become a particularly cumbersome and difficult challenge.
Thus, a system and method enabling networked computer users to discover subscriber content affinity and make corresponding recommendations is needed.